System and Method for Facilitating Bilateral and Multilateral Decision-Making

ABSTRACT

Techniques for facilitating evaluation, in connection with the procurement or delivery of products or services, in a context of at least one of (i) a financial transaction and (ii) operation of an enterprise, are disclosed. The techniques involve retrieving party and counterparty preference profile data from digital storage media; performing multilateral analyses of the combined preference data by computing a closeness-of-fit value; and delivering a list matching the selected party and the at least one counterparty using the computed closeness-of-fit values.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of application Ser. No. 13/039,660,filed Mar. 3, 2011, which is a continuation of application Ser. No.12/754,291, filed Apr. 5, 2010 (now U.S. Pat. No. 8,069,073), which is acontinuation of application Ser. No. 11/711,249, filed Feb. 27, 2007(now U.S. Pat. No. 7,725,347), which is a continuation of applicationSer. No. 11/171,082, filed Jun. 29, 2005 (now U.S. Pat. No. 7,184,968),which is a continuation of application Ser. No. 09/538,556, filed Mar.29, 2000 (now U.S. Pat. No. 6,915,269), which claims the benefit ofprovisional Application No. 60/173,259, filed Dec. 23, 1999; theserelated applications are hereby incorporated herein by reference intheir entirety.

TECHNICAL FIELD

The present invention relates to bilateral and multilateral evaluationmethods and systems.

BACKGROUND

Consumers constantly decide which products and services best satisfytheir needs and desires. Producers correspondingly decide how best toconfigure their products and services, from amongst a wide array ofchoices. They must not only choose a suitable price, but also mustdecide which combination of other attributes of their products andservices will best satisfy consumers.

In order to facilitate these decisions, there have therefore arisen avariety of marketing research techniques. Among these are forcedtrade-off or forced choice methodologies, including conjoint analysis.Through statistical methods, these techniques allow prediction of whichattributes of products and services are relatively more and lessvaluable to a given group of constituents.

Based on these conventional techniques, producers of goods and servicesare able to model buyers' or users' preferences, thereby facilitatingdesign or selection of products and processes that best satisfy thosepreferences. For persons on two sides of a transaction (a producer and agroup of consumers, for example), conventional techniques permit personson one side of the transaction to model the preferences of a group ofconstituents on the other side of the transaction. Conventionaltechniques may therefore be called unilateral, or one-sided, evaluationtechniques.

SUMMARY OF THE INVENTION

In accordance with one aspect of the invention there is provided amethod for facilitating evaluation, in connection with the procurementor delivery of products or services, in a context of at least one of (i)a financial transaction and (ii) operation of an enterprise, suchcontext involving a member of a first class of parties in a first roleand a member of a second class of counterparties in a second role. Themethod involves supplying to at least one of the parties a series offorced choice questions so as to elicit party responses; supplying to atleast one of the counterparties a series of forced choice questions soas to elicit counterparty responses; and delivering a list matching theat least one party and the at least one counterparty according toanalysis of preference profiles determined using conjoint analysis ofthe party responses and the counterparty responses.

In alternative embodiments, the list may be ranked according tocloseness of fit.

In alternative embodiments, the method may further involve supplying toat least one party or counterparty co-evaluator a series of forcedchoice questions so as to elicit co-evaluator responses, wherein thelist matches the at least one party and the at least one counterpartyaccording to analysis of preference profiles determined using suchco-evaluator responses.

In alternative embodiments, the party responses may reveal, with respectto each level of each of a first series of attributes, a utility valuewhich indicates the value that the party places on the level of theattribute. Such party responses may reveal the utility values withoutthe utility values being provided explicitly. Additionally, thecounterparty responses may reveal, with respect to each level of each ofa second series of attributes that complements the first series ofattributes, a utility value which indicates the value that thecounterparty places on the level of the attribute. Such counterpartyresponses may reveal the utility values without the utility values beingprovided explicitly.

In alternative embodiments, the series of forced choice questions and/orthe list may be obtained from a remote server over a communicationnetwork. The series of forced choice questions may be supplied by makingavailable a set of web pages providing a set of questions and permittingentry of responses thereto. The party responses and the counterpartyresponses may be provided to a remote server over a communicationnetwork.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing features of the invention will be more readily understoodby reference to the following detailed description, taken with referenceto the accompanying drawings, in which:

FIG. 1 shows a block diagram of an embodiment of a method in accordancewith the present invention for facilitating bilateral and multilateraldecision-making;

FIG. 2 shows a block diagram of a further embodiment of a method inaccordance with the present invention in which conjoint analysis isemployed;

FIG. 3 shows a block diagram of an embodiment of a system in accordancewith the present invention;

FIGS. 4 and 5 illustrate the logical flow of a method according to anembodiment of the invention, that may be implemented using a web serveron the Internet;

FIGS. 6 and 7 are histogram representations of a preference profile of aparty who is a job applicant and of a counterparty employer inaccordance with an embodiment of the invention;

FIG. 8 presents a side-by-side comparison of the preference profiles ofFIGS. 6 and 7; and

FIGS. 9, 10, and 11 are screenshots demonstrating hierarchicallystructured questions organized into three stages in accordance with anembodiment of the invention.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

By contrast with conventional methods, embodiments of the presentinvention enable a bilateral evaluation of preferences: a decision isrecommended based on its providing a relatively close fit between thepreferences of each potential pairing of party and counterparty to apotential transaction, when compared with other possible pairs ofparties to the potential transaction. Indeed, embodiments of the presentinvention may likewise be employed when information about preferences isprovided not just by two parties to the transaction (a party and acounterparty), but also by at least one co-evaluator, who provides auseful perspective on the preferences of a party or a counterparty. Inthis case, the evaluation is multilateral rather than bilateral.

In various embodiments of the present invention, there can be employedquestions that require a forced choice to reveal preferences of therespondent. The benefit of the forced choice approach is that it helpsto uncover underlying preferences that are hidden and sometimes notconsciously evident even to the respondent.

In this connection embodiments of the invention may employ conjointanalysis. See for example, Cattin, P. and R. R. Wittink, “Commercial Useof Conjoint Analysis: A Survey”, 45 Journal of Marketing 44-53 (No. 3,Summer, 1982), and “Commercial Use of Conjoint Analysis: An Update”, 53Journal of Marketing 91-96 (July, 1982); Green, P. E. and Y. Wind, “NewWay to Measure Consumers' Judgments,” Harvard Business Review, July 1975(“Green and Wind”); see also the references identified in the extensivebibliography of Patrick Bohl: Conjoint Literature Database CLD,University of Mainz, Germany, 1997. The foregoing articles andreferences are hereby incorporated herein by reference.

As used in this description and the accompanying claims, the followingterms shall have the meanings indicated, unless the context otherwiserequires:

The term “party” includes a natural person or an entity, wherein anentity may be any association, organization, or governmental agency. A“counterparty” is similarly any other natural person or an entity.

A “financial transaction” is a transaction in which services or productsare being procured or delivered under circumstances involving anexpectation that they will be paid for. Thus “financial transactions”include enrollment at a college or university or a private school(wherein educational services are rendered for tuition), employment byan entity (wherein an employee's services are rendered for payment bythe employer), engagement of a physician or health maintenanceorganization (wherein health care services are provided forcompensation), choosing a retirement community, investing in a mutualfund, taking a vacation, or in executing a merger or joint venture oracquisition. The terms “services” and “products” include the singular aswell as the plural.

An “enterprise” is a business organization (regardless of form), agovernment agency or organ, or a non-profit-organization (including areligious, scientific, or charitable organization).

“Attributes” of a product or service include characteristics, features,and benefits of the product or service. Hence (as an example) if theservice is college education, attributes may include the size of theschool, the prestige of the school, and the degree of structure of theschool's educational program.

A “level” of an attribute is a value associated with the attribute thatpertains to a characteristic, feature or benefit of a product orservice. The value may, but need not, be quantitative; the value may becategorical. Hence if the service is college education, the level of theattribute “school size” may be quantitative, as for example, “9378students”, or may be categorical, as for example, “between 5,000 and10,000.” Attribute levels may be categorized even when more abstractattributes are involved. For example, if the attribute is prestige, alevel may be “widely viewed as highly prestigious'; if the attribute isdegree of structure in the education program, a level may be “low degreeof structure”.

FIG. 1 is a block diagram of an embodiment of a method in accordancewith the present invention for facilitating bilateral and multilateraldecision-making. In this embodiment, six activities are involved. Asshown in item 11, first there is obtained from each party in a firstclass responses to a first set of questions, and the responses arestored in a suitable digital storage medium. Also, in item 12, there isobtained from each counterparty in a second class responses to a secondset of questions, and the responses are stored in a suitable digitalstorage medium. (We discuss the nature of suitable questions inconnection with later figures.) (Note items 11 and 12 need not becontemporaneous and need not be sequenced in any order.) In item 13, afirst preference profile is generated for each party, based on theparty's responses to the first set of questions; and, similarly, asecond preference profile is generated, in item 14, for eachcounterparty, based on the counterparty's responses to the second set ofquestions. The questions and the resulting profiles may be developedusing any of a wide range of approaches. In some embodiments, asdescribed below, there may be employed conjoint analysis or otherforced-choice methodologies. It is within the scope of the invention toutilize a first methodology in connection with the first set ofquestions and a second methodology in connection with the second set ofquestions. Once these preference profiles have been generated, themethod next analyzes, for each party in the first class, the preferenceprofiles of counterparties in the second class, and derives a rankedlist of counterparties that provide the closest fit of preferences withthat party, as compared with the fit of all counterparties in the secondclass (item 15). Finally, in item 16, the list of closest fittingcounterparties for each party is communicated to that party. (Similarlyfor each counterparty, the method derives a ranked list of parties thatprovide the closest fit of preferences with that counterparty, ascompared with the fit of all parties in the first class; and the list ofclosest fitting parties for each counterparty is communicated to thatcounterparty.) By providing such a list in each case, based on abilateral or multilateral preference analysis, the method facilitatesparties and counterparties in making decisions that are based on thecloseness of the fit between their preferences.

FIG. 2 shows one embodiment of a method according to the invention, inwhich, first, preference profiles for parties and counterparties aregenerated using conjoint analysis techniques. Conventional conjointanalysis techniques, used in a unilateral fashion, are described in thereferences described above near the beginning of this section of thedescription.

Once preference profiles have been generated according to the embodimentof FIG. 2, they are then used to recommend to each party a set ofcounterparties who provide the closest fit of preferences amongst thecounterparties considered. Since the embodiment uses a preferenceprofile for both parties and counterparties to evaluate the closeness offit of preferences, it is a bilateral preference analysis method asopposed to the unilateral methods of the prior art.

We now consider the embodiment of FIG. 2 in further detail. First, inthe embodiment of FIG. 2, a set of questions 201 is posed to each partyand counterparty. The questions are designed to reveal the utility valuethat each respondent places on the possible levels of a set ofattributes {a₁, a₂, . . . a_(m)} related to the proposed transaction.

Bilateral preference methodologies according to the embodiment of FIG. 2are useful in, but are not limited to, three exemplary contexts.

In the first exemplary context, an individual party wishes to enter atransaction with an organization counterparty. In the transaction, theparty seeks to identify an organization with respect to which thepreferences of such party are a good fit relative to the alternatives.The organization counterparty, on the other hand, seeks to give entry toparties who will be successful within the organization. Examples of thefirst exemplary context include a student (as the individual party)choosing colleges to attend (the organization counterparty), and anemployee (the individual party) choosing a corporate employer (theorganization counterparty).

In the first exemplary context, the questions asked of the party aredesigned to reveal the utility values that the party places on possiblelevels of a set of attributes related to the environment within thecounterparty organization. The preference profile created by the party'sanswers can thus be called a “value profile” in this context. Bycontrast, the questions asked of each potential counterpartyorganization are designed to reveal the preference profile that thecounterparty considers necessary for a party to be successful within itsorganization. The preference profile created by the counterparty'sanswers in this context can thus be called a “success profile”. In thefirst exemplary context, a decision is recommended to the party based ona relatively close fit between the party's value profile and thecounterparty's success profile.

Note that questions for a counterparty organization in the firstexemplary context are not necessarily directed to revealing profiles ofsuccessful individuals within its organization in the past. Thequestions may instead elicit the value profiles of individuals that thecounterparty believes will be successful within its organization in thefuture.

In a second exemplary context, both the party and the counterparties toa potential transaction are organizations. In the transaction, the partyand counterparty seek to join together to form one organization. Anexample of such a context is a corporate merger or acquisition. In thesecond exemplary context, the questions asked of both the party andpotential counterparties are designed to reveal the value profile thateach respondent considers necessary for success within its organization.Thus, in the second exemplary context, a decision is recommended basedon a relatively close fit between the success profiles of the party andcounterparty. In a merger example, such a recommended decision maximizes“culture fit” between merging companies.

Finally, in a third exemplary context, both the party and thecounterparties to a potential transaction are individuals. In thetransaction, the party and counterparty seek to enter a financialrelationship. For example, an individual party may seek a counterpartypartner for a joint venture. In this third context, the questions askedof the party and each potential counterparty are designed to reveal theutility value that each respondent places on possible levels of a set ofattributes related to the proposed relationship. A decision is thenrecommended based on a relatively close fit between the resulting valueprofiles of the party and a counterparty.

For convenience in this section of the description, we referpredominantly to a “potential transaction”. However, embodiments of thepresent invention may also be used in dealing with operation of anenterprise. In such a case, the party and the counterparty may (but neednot) be different constituents of the same enterprise and the issues offit between the constituents may involve, for example, addressingorganizational inefficiencies in the workplace and a wide variety ofother activities. In one example, the party and counterparty may bemanagement and labor, and the issue of fit may involve a company policyto deal with staggered work hours. Alternatively, the party andcounterparty may be a managers of two different divisions of a companyhaving competing claims on a common resource to them, such as marketing.Or, as yet another example, the enterprise may be city government, theparty may be the police force, the counterparty may be the mayor, andthe issues of fit may be related to employee benefits, including termsof a health insurance, to cover the police force. In any case, thetechnical approach for embodiments of this type is similar to thatdescribed below with respect to a potential transaction between partyand counterparty.

A common feature of questions used in each of the exemplary contexts isthat the attributes are chosen so that those of the parties “mirror,” orotherwise complement, those of the counterparties. For example, for aparty who is a college applicant deciding which college to attend, theattributes may include: population of the locality in which the schoolis located, degree of structure of the learning environment, and averageclass size. The counterparties for this transaction may be collegeadmissions personnel, and their “mirror” attributes in helping studentsto decide whether this school would provide a close fit of preferencesmay be: “students who do well here prefer being in a locality with whatpopulation”; “students who do well here prefer attending a school withwhat degree of structure of the learning environment”; and “students whodo well here prefer having classes of what average class size.”

An example of questions according to one embodiment of the invention isprovided in Table 1 for college applicants and for colleges seekingapplicants. Table 2 provides a similar set of questions for mutual fundpurchasers and mutual funds seeking investors. Table 3 provides a set ofquestions for job seekers and employers seeking job candidates.

In process 201 of the embodiment of FIG. 2, each respondent is posed Nquestions {(Q₁, Q₂, . . . Q_(N)}. As described above, questions forcounterparties typically mirror the questions for parties. Thesequestions may be fashioned in a wide variety of forms. In one form ofquestioning, each respondent is provided a series of individualmulti-attribute descriptions and asked to rate each of thesedescriptions. In another form of questioning, each respondent isprovided with a series of pairs of multi-attribute alternativedescriptions, and is asked, for each pair, to select a desired one ofthe alternatives. In yet another form of questioning, each respondentmay be asked to select from among two or more alternativemulti-attribute descriptions.

Proper design of the questions permits statistical evaluation of theresponses, from which may be derived utility values for each respondent.For example, the college applicant may be asked to rate a selection ofpotential colleges from 1 to 10, with 10 being most favorable; eachcollege may be characterized by a level for each of a series ofattributes. For example, in the case of attributes such as population oflocality, degree of structure of the learning environment, and classsize, one of the colleges to be ranked may be characterized by levels asfollows: in a locality with population 100,000, unstructured learningenvironment, and small class size.

In general, each attribute {a_(i)} will have possible attribute levelswhich characterize it—in the example, there may be possible collegelocations with populations between 15,000 and 100,000; two options forlearning environment (structured or unstructured); three class sizes(small, medium, and large), and so on. Note that the attribute levelsneed not be numbers, but may also be yes/no choices, or choices of itemsfrom a list of categories. Furthermore, note that attributes, and levelsof the attribute, may also be directed to “soft” characteristics relatedto a transaction; that is, characteristics which are more emotional innature and less quantifiable. For example, in an employment setting, arelevant attribute could be the degree of expected after-worksocializing with fellow employees, and the levels of the attribute couldbe “rare,” “moderate,” and “frequent.”

The questions {Q_(i)} need not, however, ask each respondent to evaluatea list of all possible combinations of attribute levels. Rather, the setof questions actually posed to the respondents are selected to achieve abalance across independent contributions of each attribute (or,alternatively, such that every point in the space of possible attributelevel combinations may be represented as a linear combination of thechosen combinations). In other words, the questions may be designed sothat responses to them can be analyzed in terms of attributes that, inmathematical terms, are orthogonal to one another or nearly so.

In order to increase efficiency of the process of obtaining informationfrom respondent or, to enhance the collection of information that ismost pertinent, questions may be structured hierarchically. In this way,responses for one or more questions may be used to gate the selection ofsubsequent questions. Alternatively, or in addition, questions may be insuites, with each suite dealing with a given area of inquiry. Forexample, in the college selection example, one suite of questions mayaddress factors governing the experience of life at the school such asschool size, social activities, geographic location, climate,facilities, nature of housing accommodations, and another suite mayaddress conditions associated with pursing a given major (say history orengineering) at the school (conditions such as class size, expectedhours per week studying, use of teaching assistants or use of fullprofessors). Also, for example, in the job example, one suite mayaddress company-related factors (such as expectation/participation incompany-sponsored events, expectation around consensus building,locations, and emphasis on cross-training between functions), andanother suite may address function specific matters (such as frequencyof overnight travel, work week hours, and type of job trainingprograms).

In one particular embodiment, the questions are organized into threestages. In the first stage, the respondent ranks the levels of eachattribute, in descending order of preference. For example, “1” couldsignify the most preferred level, and “3” the least preferred level, forthree possible levels of an attribute. In the second stage, therespondent is asked to rate his or her degree of preference for the mostpreferred level of each attribute, over its least preferred level; forexample, the degrees of preference could be “1, slightly preferred”; “2,moderately preferred”; “3, greatly preferred”; “4, I must have—the leastpreferred level would be upsetting.” Finally, in the third stage, aseries of two-option choices is given to the respondent, forcing therespondent to express the degree to which he or she would prefer one oftwo multi-attribute combinations. For example, the respondent could bepresented with option A and option B, each having different levels oftwo attributes, and asked to rank them on a scale of 1 to 9 (1 meaning“strongly prefer option A”, 5 meaning “the two are equal,” and 9 meaning“strongly prefer option B”). Examples of questions from each of thesethree stages are shown in FIGS. 9 through 11.

Once each respondent has provided a set of ratings {R₁, R₂, . . . R_(N)}in answer to the questions (process 202), the embodiment of FIG. 2 nextcalculates a preference profile for each respondent, which includes theutility value that each respondent places on possible levels of theattributes {a_(i)} related to the proposed transaction. The preferenceprofile is generated in process 203 by establishing, for eachrespondent, a utility function U_(i)(a_(i)) for each attribute; thisfunction provides a utility value corresponding to each level of theattribute a_(i). The utility functions are generated by firstcalculating a total utility for each example combination that was rankedby the respondent. The total utilities are calculated by evaluatingproposed utility functions for each attribute at the attribute levelscomposing each combination, and, for each combination, summing theresulting utility values. The functions are then chosen from amongst theproposed functions by the criterion that a ranking of the totalutilities should correspond to the respondent's actual rankings asclosely as possible. The result, for each respondent, is a utilityfunction U_(i)(a_(i)) chosen for each attribute {a₁, a₂, a_(m)}. Eachutility function translates each level of its attribute into a utilityfor that respondent. So, for example, a utility function will beestablished for the college applicant's evaluation of the collegelocation attribute (with a utility value corresponding to each locationA, B, and C—say 0.3, 0.2, and 0.4), the class size attribute (with autility value corresponding to small, medium, and large class sizes—say0.5, 0.2, 0.1), and so on.

As in conventional conjoint analysis methods, the utility functions arenormalized to permit comparisons between the utility values of givenlevels of different attributes. However, in conventional methods,respondents are typically treated as a class and their responses areanalyzed collectively. Here the context is typically different, and theresponses of each party (and counterparty) are typically analyzedseparately, so that for each party and each counterparty there isobtained a separate set of utility functions. Furthermore, conventionalmethods produce utility functions in a one-sided, or unilateral,fashion. For example, a producer conventionally obtains a set of utilityfunctions describing the preferences of consumers. By contrast, themethod of the embodiment of FIG. 2 produces a set of utility functionsfor the party and each potential counterparty, and continues with abilateral analysis as discussed below. However, as described below, insome circumstances the preferences of a group may be evaluatedcollectively. Also, the responses of any individual to questions may beaugmented and extrapolated on the basis of data previously obtained forsimilar individuals.

The set of utility functions associated with a respondent (be therespondent a party or counterparty) are sufficient to characterize thepreferences of the respondent. For example, there can be determined therelative importance that the respondent places on each attribute bycalculating, for that attribute, the range of the utility function overthe interval of possible attribute levels. A higher range for anattribute's utility function indicates a greater relative importance forthat attribute. As an example, consider the hypothetical in theparagraph before last; the respondent's range of utility values for thecollege location attribute was 0.2 (from a low of 0.2 to a high of 0.4);and the range for the class size attribute was 0.4 (from a low of 0.1 toa high of 0.5). Class size is therefore relatively more important forthat respondent than college location. More generally, there may bederived from the utility functions U_(i)(a_(i)) for a respondent, arange vector {R_(i)} having a series of components R_(i) correspondingin each case to the range of the utility function U_(i)(a_(i)) overlevels of the attribute a_(i).

From the utility functions of a respondent there can be similarlydetermined the level of each attribute giving rise to the greatestutility experienced by the respondent. In other words, from the utilityfunctions can be derived the attribute levels most preferred by therespondent. One may therefore determine a value vector {V_(i)} for eachrespondent, as shown in process 204. The components of the value vector{V_(i)} represent the levels of each attribute {a_(i)} that maximize therespondent's utility function with respect to that attribute. Inparticular, if, for counterparty number two, three levels A, B, and C ofattribute one (a₁) correspond to utility function values of 0.2, 0.3,and 0.4 respectively, then level C will be chosen as V₁ since it givesthe maximum utility value for this attribute.

Given the seminal nature of the utility functions, the preferenceprofile for each respondent, in this embodiment, is the utility functionvector {U_(i)(a_(i))} for each attribute {a₁, a₂, . . . , a_(m)}. Inother embodiments, the preference profile may be composed of one or moreof the value vector {V_(i)} and the range vector {R_(i)}.

Once the utility functions are generated, the process of determining thecounterparties having the closest fit with a party begins. As shown inFIG. 2, there are two alternate embodiments of the method of FIG. 2. Inthe first, called the aggregate value method, a list of counterpartieshaving the closest fit is determined by following processes 204, 205,206, and 208. In a second, alternative embodiment of the method of FIG.2, called the distance value method, the list may be generated byfollowing processes 207 and 208 (instead of processes 204, 205, 206, and208).

In process 205 of the aggregate value method, a vector is generatedcorresponding to a pairing of each counterparty with the party. Thesevectors are formed by evaluating the party's utility functions (fromprocess 203) at each counterparty's value vector levels (from process204)—that is, at the counterparty's utility-maximizing values. There isthus formed, for each counterparty paired with the party, a vector{U_(i)(a_(i))|v_(i)}, where the vertical bar notation indicatesevaluation of the party's utility function for attribute a_(i)ata_(i)=V_(i), and V_(i) is the counterparty's utility-maximizing valuefor attribute a_(i).

In process 206 of the aggregate value method, there is computed anaggregate value for each vector {U_(i)(a_(i))|v_(i)} by summing thecomponents U(a_(i))|v_(i) of the vector; i.e. by evaluating the sum

$\left. {\sum\limits_{i = 1}^{m}\; {U_{i}\left( a_{i} \right)}} \middle| {}_{V_{i}}. \right.$

In process 208 of the aggregate value method, the counterparty that,when paired with the party, produces the greatest aggregate value isidentified as having the closest fit of preferences to the party.Similarly counterparties yielding lower aggregate values when pairedwith the party are viewed as having a poorer fit of preferences to theparty. By selecting a group of the highest ranking counterparties, therecan be provided a list of counterparties having a relatively close fitof preferences with those of the party.

In the distance value version of the embodiment of FIG. 2, a list ofcounterparties providing a relatively close fit of preferences isgenerated by using a distance measure between the utility functionsgenerated in process 203 for the party and each counterparty. First, autility function vector {U(a_(i))} is generated for the party and eachcounterparty as described in process 203 above. Then, in process 207,for each possible counterparty that can be paired with the party, adistance value is generated by comparing the utility functions of thepair. For example, a linear distance value D may be computed using adistance measure as follows:

$\begin{matrix}{{D = {\sum\limits_{i = 1}^{m}\; {\sum\limits_{j = 1}^{J_{i}}\; \left\lbrack {{Abs}\left\{ \left. {U_{i}\left( a_{i} \right)} \middle| {}_{L_{j}}{- {U_{i}^{\prime}\left( a_{i} \right)}} \right|_{L_{j}} \right\}} \right\rbrack}}},} & \left\{ {{Equation}\mspace{14mu} 1} \right\}\end{matrix}$

where the distance value D is calculated for each possible counterpartypaired with the party; and where Abs { } indicates the absolute value ofthe subtraction result in the brackets; m is the number of attributes{a_(i)}; J_(i) is the number of levels of attribute a_(i); U_(i)(a_(i))is the party's utility function for attribute a_(i); U_(i)′(a_(i)) isthe counterparty's utility function for attribute a_(i); and thevertical bar notation indicates evaluation of the function at attributelevel L_(j).

In process 208 of the distance value method of FIG. 2, the counterpartythat, when paired with the party, produces the lowest distance value Pis identified as having the closest fit of preferences with the party.Similarly counterparties yielding higher distance values when pairedwith the party are viewed as having a poorer fit of preferences with theparty. By selecting a group of the lowest distance valuedcounterparties, there can be provided a list of counterparties having arelatively close fit of preferences with those of the party. While theillustration above uses a linear distance measure that is minimized,other distance measures may also be employed, including, for example, aleast-squares approach. In such a way, the embodiment of FIG. 2 allowsparties and counterparties to make decisions about potentialtransactions based on a bilateral evaluation of preferences.

While the embodiment of FIG. 2 has been described with reference to alist of counterparties being provided to a party, it should beunderstood that, given any two classes of parties denominated “parties”and “counterparties,” the embodiment of FIG. 2 can equally be used torecommend a list of parties to a counterparty; this may be accomplishedby simply following the described processes, but replacing the term“party” with “counterparty,” and vice versa. Generally, it should beunderstood that embodiments of the invention are symmetrical withrespect to two sides of a transaction, in that they may be used equallyto recommend decisions to one side as to the other.

Furthermore, where embodiments are described in which, first, apreference profile is generated for persons on one side of atransaction, and then a preference profile is generated for persons onthe other side of the transaction, it should be understood by those ofordinary skill in the art that the order of questioning the persons, andof generating the preference profiles, is not essential. Thus, where itis described to ask questions of persons on one side of a transactionfirst, and then of persons on the other, it should be understood that itis equally possible to reverse the order of questioning (by askingquestions of the opposite side of the transaction first), or even to askquestions of both sides simultaneously.

In a further related embodiment, parties and counterparties are enabledto make decisions based on a multilateral evaluation of preferences. Insuch an embodiment, the method proceeds as described for FIG. 2, exceptthat questions are asked not only of parties and counterparties, butalso of one or more co-evaluators. A co-evaluator may be any naturalperson or an entity, as with the parties and counterparties. The party,and any of the possible counterparties, may wish to include the input ofa co-evaluator as an aid to decision-making. Thus, for example, acollege applicant party may wish to have a guidance counselor or hisparents evaluate the circumstances under which he performs best, orseems most content, in order to aid him in deciding which college toattend. Similarly, a college counterparty may wish to have input fromalumni/ae, faculty, and current students to guide in selection ofstudents to admit. In a merger, a corporation party may wish to have themembers of its various departments, and even some of its customersand/or suppliers, act as co-evaluators, to assist in determining thedegree of “culture fit” with a corporate counterparty with which it ismerging.

In each case, the co-evaluator chosen has a useful perspective on theparty or counterparty's preference profile. The question array, ranking,utility function, and value vector procedures are followed as in boxes201 through 204, except that in this multilateral embodiment they areperformed for at least one co-evaluator, based on his or her ownperception of the associated party's or counterparty's preferences, inaddition to being performed by the parties and counterpartiesthemselves.

Co-evaluators may fall into two exemplary categories, although they arenot limited to these categories. In the first exemplary category, theco-evaluator provides input concerning the circumstances under which hisor her associated party or counterparty is most content or satisfied. Inthis category, the co-evaluator can be said to provide a preferenceprofile for his or her associated party or counterparty.

In the second exemplary category, the co-evaluator provides inputconcerning the circumstances under which his or her associated party orcounterparty performs best. In this category, the co-evaluator can besaid to provide a success profile for his or her associated party orcounterparty.

Attributes for co-evaluators typically mirror those for parties andcounterparties. For example, in the college-admissions example discussedin connection with FIG. 2, attributes for a guidance counselorco-evaluator could be: “Prospect does best in environments that . . . ”or “Prospect is happier with products or services that . . . ”Similarly, questions for a co-evaluator for a counterparty may bestructured to elicit answers to the questions: “People who do well heretypically like jobs that . . . ” or “Users who are satisfied with thispurchase typically prefer items that . . . ”

A co-evaluator for a party or counterparty need not be a single person;it could also be a group of people. For example, a corporatecounterparty may wish to use the members of a given department as itsco-evaluators in a transaction. In such a case, i.e. where aco-evaluator consists of a group of individuals, questions are asked ofeach member of the group of co-evaluators, and rankings are obtainedfrom each. Then a single set of utility functions (one function for eachattribute) is generated for the group of co-evaluators. This may be doneby averaging utility functions for each member of the group; byweighting some members' utility functions more highly, in a weightedaverage of functions (with the optimal weighting determined based on thecontext of the transaction); or by allowing the counterparty (or party)associated with the co-evaluator to choose which group member's profileto use as the co-evaluator's profile.

Where there is a group co-evaluator, or where there is more than oneco-evaluator for a single party or counterparty, it may also be usefulto provide a visual display of each co-evaluator's preference profile toparties and counterparties. Such a visual display could take the form ofa histogram, with a bar indicating the relative value of attributes; orthe visual display could graphically display a utility function for eachattribute, for each co-evaluator. Another useful visual display could bea scatterplot or distribution (characterized, for example, by a mean andstandard deviation) of the preference profile results from more than oneco-evaluator. By using such visual displays, parties and counterpartiesmay be enabled to weigh the input of multiple co-evaluators in acomparative and qualitative fashion.

When a party or counterparty uses a co-evaluator, methods according toembodiments of the invention may require the party or counterparty'spermission, before releasing a co-evaluator's preference profile toother respondents in the decision-making process.

Once a preference profile has been obtained for the party, eachcounterparty, and each co-evaluator, the next process in a multilateralembodiment of the invention is, as with the bilateral embodimentsdescribed above, to recommend a list of counterparties providing arelatively close fit of preferences. First it must be determined, foreach party and counterparty who used a co-evaluator, how to use theco-evaluator's preference profile in the analysis. In one embodiment,this is performed by the following algorithm:

1) Determine the closeness of fit of the party or counterparty'spreference profile with that of its associated co-evaluator. This may bedone using the aggregate value method or the distance value method (eachdescribed above for a bilateral embodiment).

2) If the profile of the co-evaluator is close enough to that of theparty or counterparty, as judged against a pre-established standard,then the party or counterparty's own profile will be used for comparisonwith potential partners to the transaction.

3) If, however, the co-evaluator's preference profile differssufficiently from that of its associated party or counterparty (asjudged against the pre-established standard), then the associated partyor counterparty is given a choice as to which preference profile to usefor comparison with potential partners to the transaction. The party orcounterparty may choose to use its own profile only, or that of theco-evaluator only, or (optionally, for an additional fee) to use eachprofile separately and obtain results using each.

Once it is determined which preference profile will be used for theparty and each counterparty, a multilateral embodiment of the inventionproceeds as described above for bilateral embodiments. The result ofthis multilateral embodiment, then, is to provide a list ofcounterparties to the proposed transaction who provide a relativelyclose fit of preferences with those of the party, in a way that takesinto account the perspective of at least one co-evaluator.

Because decisions are recommended based on the preferences of more thanone party to a transaction, embodiments of the invention areparticularly advantageous for long-term, relational transactions.Examples have been provided above of utilization of embodiments insituations where parties and counterparties may lack any previousbusiness relationship. However, such a circumstance is in no way anecessary foundation for application of embodiments of the presentinvention. For example, embodiments of the present invention may beemployed for evaluation of existing relationships between employer andemployee. Questions in such a circumstance may, for example, be directedto particular work conditions, such as scheduling of employee's workhours during the day, work rules and changes to physical facilities. Inthis manner, management and employees may usefully evaluate potentialissues of importance in the work environment. As another example,embodiments of the present invention may be applied within corporationsto determine where there is “gear grinding” within the organization(inefficient or counterproductive relationships), or areas of difficultyin “culture fit” between merged companies.

Similarly, embodiments of the present invention may be used in tandemwith more traditional evaluation techniques. For example, potentialemployees may be identified using traditional techniques, and thereafterpromising candidates along with human resources managers may besubjected to co-evaluation in accordance with an embodiment of thepresent invention.

It is equally possible to refine evaluation techniques in variousembodiments herein. One method of refinement is to consider instanceswherein a close fit has been predicted by an embodiment, but whereinexperience later shows there to be a problem. (Or alternatively, a closefit has not been predicted, but nevertheless resulted.) When the reasonfor the outcome is uncovered, it may be due to an attribute that had notbeen previously identified, or due to an ineffective or badly wordedquestion. In such cases, the questions posed to respondents may bemodified to take into account a new attribute or to correct ineffectivequestions. The questions may then be used for new submissions to futurerespondents or can optionally be resubmitted to former respondents.Alternatively, or in addition, the problem may be attributable toimproper analysis of the answers to the questions, and these matters canbe adjusted by modifying, for example, the utility functions associatedwith the party or counterparty as appropriate, and re-performing theanalysis.

FIG. 3 shows a block diagram of an embodiment of a system in accordancewith the present invention. A first question and response module 302obtains responses from each member of a first class of parties 301 to afirst set of questions. The questions are designed to elicit revelationof preferences that can be used to estimate the closeness of eachparty's fit with potential counterparties to the transaction. The firstquestion and response module then stores the party's responses in afirst digital storage medium 303.

Similarly, a second question and response module 305 obtains responsesfrom each member of a second class of counterparties 304 to a second setof questions. These questions are, similarly, designed to elicitrevelation of preferences that can be used to estimate the closeness ofeach counterparty's fit with potential parties to the transaction. Thesecond question and response module then stores the counterparty'sresponses in a second digital storage medium 306.

A first profile processor 307 uses the responses stored in first storagemedium 303 to derive a first preference profile for each party, and asecond profile processor 308 uses the responses stored in second storagemedium 308 to derive a second preference profile for each counterparty.

A closeness-of-fit analyzer 309 analyzes the preference profilegenerated for each party by first profile processor 307 in relation tothe preference profiles generated by second profile processor 308. Foreach party, the result is an output ranked list 310 of counterpartiesproviding a relatively close fit of preferences with that party,compared with the other potential counterparties. The closeness-of-fitanalyzer communicates such a list to each party.

In embodiments of systems according to the invention, the first andsecond question and response modules 302 and 305, the first and secondprofile processors 307 and 308, and the closeness-of-fit analyzer 309may be implemented as computer processes running on multiple computersin communication with each other (for example over a network, includingthe Internet), or as processes running on a single computer. Similarly,the first and second digital storage media 303 and 306 may be separatestorage devices, or portions of a single digital storage medium.

In a preferred embodiment, the system of FIG. 3 is implemented as a hostcomputer accessible over a network, such as the Internet. In particular,parties 301 and counterparties 304 may access the system using remotecomputers which are in communication with a host computer via Web pagesof a web site on the World Wide Web. The host computer is then a webserver, which runs computer processes that implement the first andsecond question and response modules 302 and 305, the first and secondprofile processor 307 and 308, and the closeness-of-fit analyzer 309.The server stores responses to questions in an associated contentstorage device (for example at least one hard disk drive), which servesas first and second storage media 303 and 306. The server maycommunicate with parties and counterparties using e-mail, or by makinginformation available on a web site, or by other communication methods.

Further information concerning the Internet and E-mail (both of whichterms are used throughout this specification) is provided, for example,in Gralla, How the Internet Works (Ziff-Davis Press, 1996), which ishereby incorporated by reference; see especially pages 44-49.

In further embodiments of systems and methods according to theinvention, communication with a server and information processing may beimplemented using wireless devices.

FIGS. 4 and 5 illustrate the logical flow of a method according to anembodiment of the invention that may be implemented using a web serveron the Internet. This embodiment also illustrates use of the processesdescribed above in connection with the system of FIG. 3.

In box 401, a system, which may be a website server on the Internet,receives primary data from parties and counterparties, via guidedtemplates for data entry. Each party or counterparty enters the site,registers basic information (for example name, address, and othercontact information), and selects a decision area from a set of pre-setparameters. The pre-set decision areas may be, for example, collegeselection or employment searching. For college selection, the partycould be a college applicant, and the counterparty may be a collegelooking for or decided which students to admit; for employmentsearching, the party may be a job candidate looking for a job, and thecounterparty may be an employer looking for employees or decidingamongst candidates. After receiving the decision area choices, thesystem prompts the party or counterparty, via guided templates, forinformation on co-evaluators that he or she wishes to include in thedecision-making process. The system also gives the party or counterpartythe option of using data from only the co-evaluators in making thedecision (with no input from the party or counterparty himself).Finally, the party or counterparty authorizes payment, and the systemreceives and verifies the payment method (for example, credit cardpayment).

Next, in box 402, the system prompts each party and counterparty, viaguided templates, for supplemental data that might be useful later inthe process of evaluation. For example, a job candidate party may beprompted for, and register, a formatted résumé. A college applicantparty may be prompted for, and register, a summary of his academichistory. In each case, the prompted supplemental data is potentiallyuseful to a counterparty (e.g. an employer or a college) later in theprocess of evaluation (described below). Similarly, the system promptscounterparties for supplemental data that are potentially useful toparties later in the decision-making process. For example, if thecounterparty is a company searching for job candidates, the company'ssupplemental data may be “leads” on housing opportunities, which wouldbe attractive to job candidates who need to find housing near thecompany. Once the parties and counterparties have entered supplementaldata, the system assigns a unique identifier to each user, includingparties, counterparties, and any co-evaluators that they have named. Thesystem also creates a file for each user, and associates each file withthe corresponding unique identifier.

The system next, in box 403, disseminates a questionnaire form to eachparty and counterparty, along with the unique identifier assigned toeach. (This process is omitted for a party or counterparty who haselected to have evaluation performed by a co-evaluator only, asdescribed above in connection with box 401.) The system then administersthe questionnaire forms to each party or counterparty. For example, thesystem may guide the party or counterparty through a series of questionsformatted as templates on Web pages on a web site, through which thesystem receives each party's (or counterparty's) answers to thequestions. The questions on the questionnaire form are designed toelicit the utility value which the respondent places on possible levelsof each attribute, without necessarily asking for preferences directly.For example, the questions may be structured to elicit from the partiesanswers to the questions: “I do best in environments that . . . ” or“I'm happier with products or services that . . . ” Similarly, thequestions for counterparties may be structured to elicit answers to thequestions: “People who do well here typically like jobs that . . . ” or“Users who are satisfied with this purchase typically prefer items that. . . ”

As previously discussed, Tables 1-3 show examples of questions that maybe used, in various embodiments of the invention, for elicitingpreferences in the areas of college selection (Table 1), mutual fundselection (Table 2), and employment selection (Table 3).

In box 404, the system disseminates a questionnaire form to eachco-evaluator, and administers the form to the co-evaluator, in a fashionsimilar to that described for box 403. (If no co-evaluators were namedfor a given party or counterparty, then this process is omitted). Thequestions for a co-evaluator are structured to elicit the co-evaluator'sperspective on the associated party or counterparty's preferences,without necessarily asking for preferences directly. The questions may,for example, elicit input concerning the utility value which theassociated party or counterparty places on possible levels of eachattribute; or may elicit input concerning the circumstances under whichthe associated party or counterparty performs best. For example,questions for a co-evaluator for a party might be structured to elicitanswers to the questions: “Prospect does best in environments that . . .” or “Prospect is happier with products or services that . . . ”Similarly, questions for a co-evaluator for a counterparty may bestructured to elicit answers to the questions: “People who do well heretypically like jobs that . . . ” or “Users who are satisfied with thispurchase typically prefer items that . . . ”

In box 405, the system reviews for internal consistency the completedpreference forms that it received from boxes 403 and 404. For each form,when the extent of logical inconsistency exceeds a desired level, thesystem communicates the fact of inconsistency to the respondent whocompleted the form, and asks whether he or she wishes to fill out theform again. A stark example of such an internal inconsistency is where arespondent has answered three questions, in the same answer form, withthe answers “I prefer A to B”; “I prefer B to C”; and “I prefer C to A.”Checks of internal inconsistency are useful, for example, in detectingrespondents who are attempting to “game the system,” by providinganswers that show preferences for given attributes, when in fact theirpreferences are otherwise; often, in such a case, the respondentinadvertently answers questions in an inconsistent fashion. If theinconsistent form was completed by a party or counterparty, then theparty or counterparty is also given the option of allowing the processto continue using only input from co-evaluators. If a respondent whofilled out an inconsistent form does not respond to a request to fillout the form again, then the process continues without that respondent'sinput.

Next, in box 406, the system sends to each party such party's preferenceprofile and profiles of any co-evaluators and to each counterparty suchcounterparty's preference profile and profiles of any of suchcounterparty's co-evaluators. The aggregated profile reveals to theparty or counterparty the results of performing a forced-choiceanalysis, or other preference analysis (including conjoint analysis)using the party's answers to the preference form questions. Thus it mayreveal to the party or counterparty the weight that he or she places onattributes that were analyzed, or the levels of each attribute that hemost preferred, or the utility value that he or she places on possiblelevels of each attribute, as determined by the preference analysis.Optionally, such information may be presented in a histogram or othergraphical display, in order to visually display the results of theanalysis. An example of such a histogram for a job applicant is shown inFIG. 6; a corresponding histogram for a counterparty employer is shownin FIG. 7; and a histogram showing a side-by-side comparison of the twois shown in FIG. 8. Note that histograms may be used to display weightsor values or both; they are used for values only where the values inquestion are quantitative (as opposed to categorical or yes/no)variables.

The party or counterparty is also given information about significantgaps between the results of his preference analysis and the results ofhis co-evaluators, either in weighting of attributes, or in mostpreferred attribute levels, or both. Such gaps may optionally bedisplayed by a side-by-side comparison of histograms, as is illustratedby FIG. 8. Knowing these gaps may lead a party (or counterparty) tore-examine its conception of its own preferences; a large gap betweenthe respondent's own perception of its preferences as compared with thatof others may mean that the respondent was not truly aware of its ownpreferences. Accordingly, the party or counterparty (as the case may be)is given the choice of using its own preferences or those of one of itsco-evaluators, as described further below.

As described below in connection with box 513, it is possible to permiteach party and counterparty to update its preference profile; when thereis a decision to update, the process must accommodate the collection ofnew preference data to provide a new analysis that will differ from theoriginal analysis if responses to the questionnaire form are differentfrom the original responses. In presenting the option to update, thesystem sends the party or counterparty his original decision areachoice, and gives him the opportunity to revise the choice (therebyreturning to box 401). The system asks the party or counterparty forauthorization to proceed to the process of looking for relatively closefits amongst a pool of counterparties or parties (respectively). Thesystem also gives the party or counterparty the option of repeating thepreference form processes (thereby repeating boxes 403 through 405), orof adding or dropping co-evaluators (thereby returning to boxes 402 and404-406). (The addition of a co-evaluator or the updating of the profilemay optionally trigger the requirement of paying an extra fee.)

In box 407, the system obtains authorization from each party andcounterparty to release the results of the search for relatively closefits. Each party has three or more options, including: a) to receive theresults, without the same information being sent to any counterparties;b) to receive the results, and to have the results sent tocounterparties with name or other key identifying data on the partywithheld; or c) to receive the results, and to have the results sent tocounterparties with full information on the party. Each counterparty isgiven corresponding options for release to parties (including the optionto withhold the counterparty's name or other key identifying data fromthe parties).

Next, in box 408, the system generates, and communicates to each partyand counterparty, a ranked list of relatively close fits for that partyor counterparty amongst the pool of reciprocal parties, based on the useof a bilateral or multilateral preference methodology. This list maycontain network addresses, web links or e-mail addresses, or othermethods of contacting reciprocal parties on the list. Also, it maycontain a listing of what information about the recipient has been sentto each of the reciprocal parties on the list, in accordance with theauthorization received in box 407 (above).

In box 409, the system facilitates action by parties and counterpartiesto identify and contact reciprocal parties. For example, the system mayenable a party to contact a counterparty by using a web link on a website, or by using a web link sent to the party as part of an e-mail; orthe system may provide phone numbers or other contact information, asauthorized in box 407 (above).

Continuing with box 510 in FIG. 5, the system next queries each partyand counterparty as to what decisions it has made in the decision areafor which the analysis was performed; for example, a party could beasked what job he or she accepted, or what product he or she selected;and a counterparty could be asked which job candidates it selected foremployment. These decision inquiries are repeated at time intervals thatare either chosen by the registrant in the primary data entry process ofbox 401, or are specified to the registrant during the primary dataentry process, or are otherwise scheduled by the system.

In box 511, the system communicates a query to each party andcounterparty as to its satisfaction with the decision that it made,after it has been informed that the party or counterparty has made thedecision. This decision satisfaction inquiry is performed at a timeinterval after process 510 that is either chosen by the registrant inthe primary data entry process of box 401, or pre-determined in thesystem.

Next, in box 512, the system, in one embodiment, performs apost-decision analysis. It analyzes key attributes that contributed toeach party and counterparty's degree of satisfaction, by comparing eachone's reported degree of satisfaction (from box 511) with the analyzedpreference form results obtained in boxes 406 and 408. The systemcommunicates to each party and counterparty its individual post-decisionanalysis, and provides each with a structured opportunity to respond tothe analysis, e.g. by providing a set of web-page templates enabling theparty or counterparty to comment on the key attributes identified in thepost-decision analysis. Additionally, the system stores the results ofthe post-decision analysis, and the comments on it. Owing to thecollection, in the course of practicing embodiments discussed in thisdescription, of substantial quantities of data that tend to be of apersonal nature, it is within the scope of various embodiments topreserve the confidentiality of such data and to disclose such data onlyunder circumstances to which the affected individuals and organizationshave given their consent.

Large discrepancies between a preference form analysis and apost-decision report may indicate that the respondent did not understandits own preferences well. Thus, such post-decision reports may helpparties and counterparties to learn about themselves, and therefore tomake better decisions in the future.

Results of post-decision analyses may be used to revise the system'smethod of preference form analysis, or to revise the questions which areasked in each decision area. For example, if it is discovered that somecollege applicant parties have decided to attend colleges with whichthey were unhappy, and some were unhappy based on attributes that thepreference form did not elicit, then the preference form for the collegechoice decision area could be altered to incorporate the overlookedattributes.

As part of the post-decision analysis, the system may also provide aco-evaluator with a report on the party or counterparty's reporteddegree of satisfaction. For example, a college guidance counselorco-evaluator can be provided with a report on a college applicantparty's (or a group of parties') degree of satisfaction, so that thecounselor can modify his or her counseling in the future.

Note, however, that in some contexts it is preferable to guarantee thata party's post-decision report will be kept in confidence with respectto (at a minimum) the counterparty with which the party entered atransaction (and vice versa for a counterparty's confidences). Forexample, it is preferable to guarantee confidentiality to an employeeparty who reports dissatisfaction with an employer counterparty in apost-decision report.

In box 513, the system invites each party and counterparty to update itspreference profile. If the party or counterparty agrees, the processbegins anew, beginning with box 401, above. For such updates, theprocess retains the data from the original analysis process, and updatesit according to the new input which the party or counterparty provides.The pricing to users may be configured so that additional charges may bemade for updates, as opposed to original analyses. A party orcounterparty may also initiate the update process itself, without aninvitation; this may, for example, be implemented by providing an updateoption for registrants on a web site. As part of an update, the systemalso allows a party or counterparty to add or delete co-evaluators. Ifthe update option is not selected, the process proceeds to box 514.

In box 514, the system invites each party and counterparty to perform anew matching process for closeness of fit, based on its currentpreference profile. If the party or counterparty agrees to do so, theprocess begins anew at box 407, with the pricing changed accordingly.

Although this description has set forth the invention with reference toseveral preferred embodiments, one of ordinary skill in the art willunderstand that one may make various modifications without departingfrom the spirit and the scope of the invention, as set forth in theclaims.

1. A computer-implemented method for facilitating evaluation, inconnection with the procurement or delivery of products or services, ina context of at least one of (i) a financial transaction and (ii)operation of an enterprise, such context involving a first class ofparties in a first role and a second class of counterparties in a secondrole, the method comprising: in a first computer process, retrievingfirst preference data from a first digital storage medium, the firstpreference data including attribute levels derived from choices made byat least one of the parties in the first class; in a second computerprocess, retrieving second preference data from a second digital storagemedium, the second preference data including attribute levels derivedfrom choices made by at least one of the counterparties in the secondclass; in a third computer process, for a selected party, performingmultilateral analyses of the selected party's preference data and thepreference data of each of the counterparties, and computing acloseness-of-fit value based thereon; and in a fourth computer process,using the computed closeness-of-fit values to provide as an output arecommendation to the selected party of at least one of thecounterparties for such procurement or delivery of products or services.2. A method according to claim 1, wherein the recommendation is a listwherein the at least one of the counterparties is ranked according tocloseness of fit.
 3. A method according to claim 1, further comprisingreceiving co-evaluator choices made by a party co-evaluator or acounterparty co-evaluator, wherein the recommendation to the selectedparty of the at least one of the counterparties is determined accordingto a multilateral analysis of preference data determined using suchco-evaluator choices.
 4. A method according to claim 1, wherein theparty choices reveal, with respect to each level of each of a firstseries of attributes, a utility value which indicates the value that theparty places on the level of the attribute.
 5. A method according toclaim 4, wherein the party choices reveal the utility values without theutility values being provided explicitly.
 6. A method according to claim4, wherein the counterparty choices reveal, with respect to each levelof each of a second series of attributes that complements the firstseries of attributes, a utility value which indicates the value that thecounterparty places on the level of the attribute.
 7. A method accordingto claim 6, wherein the counterparty choices reveal the utility valueswithout the utility values being provided explicitly.
 8. A methodaccording to claim 1, wherein at least one of the first preference data,second preference data, and the list is obtained from a remote serverover a communication network.
 9. A method according to claim 1, whereinthe party choices and the counterparty choices are provided to a remoteserver over a communication network.